Richard Barnwell

One key reason why healthcare technologies like AI, telemedicine, and software haven't structurally lowered costs or broadened access for the entire system after decades of experimentation (excluding pure financial engineering of payer contracts) is the limited reimagining of care delivery itself, with asynchronous telemedicine as a notable exception. In the same way that digitizing physical paper failed to exploit the capabilities of a new computing medium for word processing, hosting physician-patient conversations over video does not transform the actual delivery of care. In telemedicine, many of the inputs (e.g., clinician time) and therefore cost structures of in-person care persist, which is why we often see fee-for-service telemedicine companies struggle to achieve economies of scale (i.e., growing revenues non-linearly while growing costs linearly). Until we deconstruct the components of a medical appointment – from intake and triage, to chart review, history gathering, physical examination, diagnosis, treatment, and follow-up – we will continue to be underwhelmed by the disconnect between the transformative potential of these technologies and real-world patient/system impact. I’m optimistic that the emergence of reliable and performant medical AI agents will urge us to perform this deconstruction and redesign the fundamental building blocks of a “medical appointment.”

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Oracle Health announced plans to launch a next-generation EHR platform in 2025, incorporating advanced cloud and AI capabilities. This new EHR will feature Oracle's clinical AI agent, voice-activated navigation, and Health Data Intelligence, which integrates patient data from over 300 sources to enhance clinical workflows and improve patient outcomes. The platform aims to replace traditional EHRs, which often dictate rigid workflows, with a more dynamic system that allows for personalized care plans. Oracle Health emphasizes that the new EHR is a complete reinvention rather than an upgrade of the previous Cerner system. Additionally, Oracle plans to become a Qualified Health Information Network to facilitate standardized data sharing across the healthcare ecosystem. Executives at Oracle also unveiled new cloud applications to improve patient management and streamline front-office operations, reinforcing the company's commitment to enhancing healthcare accessibility and data utility while addressing interoperability challenges in the industry.

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How to improve #interoperability through #FHIR? Efficient interoperability is key to the democratization of data, but smaller EHRs have been slow to join the movement. CapMinds and Vozo have helped its #ehr clients especially smaller one to join the #fhir movement.

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As the discourse shifts from models to compound AI systems / agents, we need better AI benchmarks to evaluate multi-modal and multi-step task performance, especially in healthcare and life sciences. When we wrote the first paper demonstrating ChatGPT's performance on the USMLE, we chose the US Medical Licensing Exam as a benchmark for accessibility, speed, and ease. This benchmark was never intended to represent AI model performance on real-world clinical tasks. Today, I still see so many research teams and startups using benchmarks (like the USMLE) that are ill-fitted for assessing the true clinical or scientific performance and utility of the models they are developing for real-world contexts. Benchmark development may be seen as a "less sexy" area of research, but it is of paramount importance. Years after the rise of the transformer, we still lack adequate benchmarks for so many single-step tasks in biomedicine. With compound AI systems (i.e., architectures that integrate multiple AI models to perform complex tasks) emerging, we need new benchmarks for agentic behaviors. I'd argue that developing an agent with novel capabilities without at least proposing a companion benchmark (if an industry standard does not yet exist) may hinder the adoption of said agent, especially for high-stakes workflows. Designing more benchmarks that capture/simulate real-world clinical and scientific workflows will help us mitigate the major discrepancies observed between in silico and in vivo performance and better support safe + effective deployment of AI in biomedicine. There are already brilliant people focused here, and we need more. DMs are open if you're researching or working in this area of multi-step/multi-modal benchmarking in healthcare and life sciences! #healthcare #ai #artificialintelligence #generativeai

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Oracle Health, which acquired Cerner two years ago, just announced the launch of a next-generation electronic health record (EHR) platform in 2025, showcasing the company's latest advancements in cloud technology and AI capabilities. I wrote down a few thoughts. 1️⃣ 𝐀𝐟𝐭𝐞𝐫 𝐭𝐰𝐨 𝐲𝐞𝐚𝐫𝐬 𝐢𝐧 𝐬𝐭𝐞𝐚𝐥𝐭𝐡 𝐦𝐨𝐝𝐞, 𝐭𝐡𝐞 𝐂𝐞𝐫𝐧𝐞𝐫 𝐚𝐜𝐪𝐮𝐢𝐬𝐢𝐭𝐢𝐨𝐧 𝐢𝐬 𝐟𝐢𝐧𝐚𝐥𝐥𝐲 𝐠𝐚𝐢𝐧𝐢𝐧𝐠 𝐟𝐮𝐥𝐥 𝐦𝐨𝐦𝐞𝐧𝐭𝐮𝐦. After the big acquisition, nothing seemed to be happening. I even started thinking it was a $28B flop and that Oracle was simply moving on. Oracle stressed that the new platform is not a "refurbished Cerner EHR," but a reinvention of a digital medical record system: "You can’t bolt new innovation onto something built in the 1990s". 2️⃣ 𝐒𝐚𝐦𝐞 𝐬𝐚𝐦𝐞 𝐛𝐮𝐭 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 Even though the Tech is new, the philosophy remains the same. EHR incumbents will try to increase their (already too high) power in the hospital Tech stack. The solution won't come from incumbents. 3️⃣ 𝐌𝐨𝐫𝐞 𝐜𝐨𝐦𝐩𝐞𝐭𝐢𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐄𝐩𝐢𝐜 𝐢𝐬 𝐚𝐥𝐰𝐚𝐲𝐬 𝐠𝐨𝐨𝐝 𝐚𝐧𝐝 𝐎𝐫𝐚𝐜𝐥𝐞 𝐬𝐞𝐞𝐦𝐬 𝐭𝐨 𝐡𝐚𝐯𝐞 𝐠𝐨𝐨𝐝 𝐢𝐧𝐭𝐞𝐧𝐭𝐢𝐨𝐧𝐬 In May, an Oracle executive, Ken Glueck, executive vice president, wrote in a blog post that "Epic’s CEO Judy Faulkner is the single biggest obstacle to EHR interoperability." More pressure on Epic to open its system will always be a good thing. 4️⃣ 𝐖𝐞 𝐚𝐫𝐞 𝐞𝐧𝐭𝐞𝐫𝐢𝐧𝐠 𝐭𝐡𝐞 𝐩𝐨𝐬𝐭-𝐄𝐇𝐑 𝐞𝐫𝐚 As Tomaz Gornik, co-chair of OpenEHR (non-profit community interest organisation that aims to enable clinical healthcare to advance by better use of information and IT): "Today's solutions tightly couple data to applications. As health and care data is for life, it needs to outlive applications, so there is a clear need to separate them." The Catalonia region in Spain is a frontrunner and is setting new standards with its digital health strategy and new information system model based on openEHR. 5️⃣ 𝐇𝐨𝐬𝐩𝐢𝐭𝐚𝐥𝐬 𝐡𝐚𝐯𝐞 𝐚 𝐨𝐧𝐞-𝐨𝐟𝐟 𝐨𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐲 𝐭𝐨 𝐫𝐞𝐠𝐚𝐢𝐧 𝐜𝐨𝐧𝐭𝐫𝐨𝐥 Hospital executives need to understand the new tech dynamics and create an operating model that lets them stay in control, leverage the best technology, and keep it manageable to maintain (yes, not an easy task). In Germany, some innovative hospitals are making steps into the right direction: ➡️ A big private hospital chain is building its own EHR ➡️ Another hospital chain aims to use the patient portal as the backbone for designing digitally-enabled care journeys (on top of the EHR) ➡️ A public hospital chain is building a vendor neutral interoperability layer #hospital #ehr #epic #healthcare #healthtech

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🗝 Key Variables and Strategies Beyond LLM Selection 🚀   I have observed customers spend disproportionate amount of time deciding which Large Language Model (LLM) platform they want to commit to for their first Generative AI (GenAI) application. As they near the end of their journey they realize that LLMs by themselves account only for say 15% of the overall project effort! And then I witness another customer go through the same process. While the selection of a suitable LLM is a critical first step in GenAI application development, there are several other vital aspects to consider for a successful and differentiated GenAI strategy. Let me share a few for consideration:   Many have said it before and I will echo their words of wisdom– ‘there will be no one model to rule them all’. Historical examples from various industries, such as search engines, internet browsers, cell phone manufacturers, and car manufacturers, have shown that while first movers often play a crucial role in introducing new concepts and technologies, they don't always maintain their leadership position in the long run. As market needs evolve, new players frequently emerge with innovative solutions better suited to address the changing landscape.   LLM fitment warrants a balance between inference cost, quality of output, and latency. Balancing these three factors is essential for any GenAI implementation. I have witnessed several customers who complained about cost overruns as their GenAI application user base and use cases grew. Across multiple customer engagements, I've noticed a common pattern: to balance cost, quality, and latency effectively, customers often find it beneficial to leverage multiple LLMs within a single use case, with each LLM selected for specific actions or needs based on their unique strengths and trade-offs. For example: A request to see corporate employee vacation policy does not need the biggest most expensive LLM inference call!   Also, you will realize that the quality and relevance of your data play a crucial role in your GenAI application’s success. Data Governance, RAG implementation, and Context fine-tunning not only improves performance but also enables organizations to create unique, tailored AI applications that cater to specific business needs. This is an iterative process and takes cycles. An emerging best practice is to integrate existing keyword index search systems with RAG implementations. In many cases, end-users are accustomed to searching information using these systems, and a hybrid approach that incorporates this context has proven to yield more relevant and complete results. As such, I encourage all my customers to observe how end users searched the same information before GenAI and explore a hybrid RAG implementation that pulls data from these existing systems for additional context. 💬 How much time is your team spending on LLM selection? 📢 Subscribe to my newsletter here: https://lnkd.in/g3bdneR7 #genai #ai #aws

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Several important points for CEOs from the research of the IBM Institute of Business Value with US-based executives: * There’s no such thing as an all-purpose gen AI model - Give some teams the sledgehammer and others the scalpel * Gen AI costs are completely in your control - Find your own gen AI sweet spot * Gen AI advantage is fleeting - Make your models work harder https://lnkd.in/gVyNrQmJ Shobhit Varshney provided his point of view on this research in an interview with Sean M. Kerner in a VentureBeat article: https://lnkd.in/g4SurTTx

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17 reasons I loved #GTM24! 17. Sonal Mane could be a standalone reason to love this event! Her future of GTM AI and automation session with John Hessinger identified things GTMers should be experimenting with and how to incorporate GenAI into existing workflows. 16. Peter Ward’s discourse on formalizing market readiness and ways to align cross-functional groups. 15. Macy Berg Mody’s example of eliminating repetitive x-team readouts and making meetings more productive with shared tech informing multiple groups on customer feedback. 14. Holly Holbrook's best practices for internal comms like creating a “front door” for cross-team conversations and ideation. 13. Emi Wayner’s practical guide to GTM launch metrics was great, but it was even better to hear how she evolves metrics while retaining focus on key impact areas. 12. I loved Candice Sparks and Lexie Knauer’s dialogue on building for parity vs. differentiation. Don’t let your product get in the way of a good story! 11. The most POV-altering moment of the conference (for me) was the fireside chat on unlocking brand growth with Maya Shah-Ceccotti, Karen Sage, and Nidhi D.. It changed my thinking re: CLG. Build it into growth motions intentionally upfront! 10. “If you build it with them, they are already there.” - Kasey Stinson. 9. 43% of buyers desire a “seller-free” sales experience, according to Gartner (and Filip Szymanski in his session on PLG vs. SLG). I appreciated his thoughts on scaling growth loops, whether collaboration growth loops or user content growth loops. 8. More people would remotely lead successful GTM teams if they followed Paige Lapen’s “make work rock” framework or James B. Stanton’s weekly meeting cadence: Commit - “CFC” - Complete. 7. Internal messaging gets diluted with multiple communication methods, as Somesh Chablani pointed out. Don’t drop it into Slack and assume everyone knows. Overcommunicate with remote teams. 6. Daniel Kuperman’s competitive intelligence matrix and launch blueprint could have been an entire session, and I still would have wanted more! 5. Nonya Collier is the collaboration queen! Co-creation in a brain trust, codifying insights, amplifying the collective intel. 4. “Beyond the Launch” by Ed Cho may have convinced me that math can be fun and definitely convinced me of the necessity of focusing on pipeline maturation and not just pipeline creation. 3. Ariel Cai walked through how AI is redefining the go-to-market playbook. 2. I was excited to share my session on accelerating sustainable growth with ideal customer profiles. Plus, in a shocking turn of events, I got to chair the second day! 1. The number one reason to attend any event from The Alliance/GTM Alliance is to connect with the most incredible people! I can’t list everyone, but I have to mention Chase Payne, Cinthia Silva, Jan, Amy 🐆, Rohit Angle, Alisha Johnson, Catherine Knoll, Samantha, Sasha, and I finally got to meet the mastermind behind it all: Jack Debono!

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70% of hospitals participate in healthcare interoperability. Healthcare #interoperability is growing across hospitals, but gaps remain, especially in data exchange with long-term care and behavioral health providers. CapMinds offered health information exchange(#hie) and health interoperability (#fhir, #hl7, #mirthconnect) to its various clients across the specilaity including #behavioralhealth #mentalhealth

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Great post! My thoughts on the impact: Canada’s current push for national standards in data interoperability is promising, but the reality is complex. While the idea of seamless data sharing across provinces and territories sounds ideal, it’s challenged by Canada’s fragmented healthcare system, where each region operates under its own policies. Standardization could streamline data access, but if driven by large EHR vendors, it risks becoming too restrictive, potentially centralizing control and limiting true accessibility. In the end, while EHR systems often talk about interoperability, they mostly focus on data within their own platforms. This approach risks creating silos between hospital and community care, limiting the vision of a connected health network. True interoperability needs not just technical standards but also a commitment to open access that fosters an innovative and competitive market. We need drivers and incentives that constantly push us to strive for better care—not another solution that holds us hostage to outdated systems we’ve over-committed to.

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👉🏼 Concordance of ChatGPT With American Burn Association Guidelines on Acute Burns 🤓 Sumaarg Pandya 👇🏻 https://lnkd.in/eJhe8Bxp 🔍 Focus on data insights: - 📈 The study involved twelve acute burn care questions, demonstrating the potential for AI in clinical settings. - ✅ ChatGPT responses scored an average of 4.56 ± 0.65, indicating exceptional quality and high concordance with established guidelines. - 🧑‍⚕️ Three burn surgeons independently evaluated responses, underscoring the reliability of AI-generated information compared to expert human evaluations. 💡 Main outcomes and implications: - 🤝 ChatGPT can be integrated as a complementary tool in medical decision-making and patient education. - 📚 The findings suggest AI could streamline processes and enhance the delivery of care in burn management. - 🚀 There is potential for broader applications of AI in various medical fields, possibly setting new standards for information accuracy and accessibility. 📚 Field significance: - 🌐 This research opens pathways for incorporating AI technology into healthcare frameworks, emphasizing the need for robust evaluation of AI tools. - ⚖️ It highlights the balance between human expertise and automated systems in achieving optimal patient outcomes. - 🔄 As healthcare evolves, integration of AI may transform practices and improve patient education methods, positioning technology as a critical ally. 🗄️: [#ChatGPT #BurnCare #HealthcareAI #ClinicalGuidelines #PatientEducation #MedicalDecisionMaking #ArtificialIntelligence #ClinicalPractice]

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Somewhat predictably, the headlines of Epic's UGM were mostly focused on AI. In my mind, the ones to focus on got buried as footnotes. Products leveraging Epic's position as the dominant inpatient EHR and selling differentiated products as a wedge into new markets (mostly under Health Grid) should frighten other healthcare vendors more than the AI arms race (which they'll largely win as the default option): - Payer Platform: A Health Grid product for payers. This was decently well covered, but that's because it's a mature product at this point. A lot of outlets covered it as if it was net new, but it was launched in 2019. The most noteworthy piece is Judy's quote saying they’re investigating submitting claims directly through Epic so you don’t need a clearinghouse - this represents a threat to clearinghouses and could represent the expansion from Payer Platform as a beachhead to attack incumbent SaaS, claims processing systems. - Epic Aura (labs): This is a Health Grid product for labs. This was launched previously. I don't see any announcements, but this is a beachhead for Epic to sell to labs that have or built their own LIS, allowing them to eventually replace it over time (or at least have all major labs as customers). This threatens existing LISes even more than Epic's LIS product, Beaker. They also announced a planned Blood Bank module, which increases their appeal as an all-encompassing LIS in this push. - Epic Aura (medical devices): A new sub-category of Aura (and Health Grid) for expanding into medical devices. I was waiting to see if they'd launch a separate category for medical devices or reuse Aura. First devices are cardiac arrhythmia and CGM. This threatens businesses like Redox to some degree. - Per Healthcare IT today, "Judy said they’re working on better surgical device manufacture planning leveraging the Epic schedule." This sounds like further expansion of Aura/Health Grid to surgical supply manufacturers. - I was surprised to not hear any updates about Discovery, Epic's Health Grid program for pharma. I'm curious what shape that will take. - I thought the use of the state of Washington + the Garden Plot model is a smart distribution play. Garden Plot has existed since at least 2022, but using a state for distribution to get to new markets that Epic struggles to sell to (small clinics, behavioral health clinics, correctional medical facilities and tribal facilities) is again another example of their expansionary mode. Health Grid products will be more impactful than the Vendor Services rat races or AI wars. It's worth paying attention to and taking advantage of: https://lnkd.in/gimisDzZ

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Understanding the FHIR Patient Resource: A Review of Key Components and US Core Specifications for the Novice The FHIR (Fast Healthcare Interoperability Resources) designed to capture essential patient demographics and administrative information, it ensures interoperability across diverse healthcare systems. This resource conforms to the US Core Implementation Guide, which provides additional constraints to ensure compliance with US regulations. Profile Overview  The base Patient resource contains core fields such as name, gender, birth date, address, and contact details. In the US, implementations typically follow the US Core Patient Profile, which tightens constraints to ensure compliance with ONC’s 21st Century Cures Act. This profile requires specific fields (e.g., race, ethnicity, and birth sex) to meet US reporting standards. Extensions  FHIR’s flexibility allows for extensions that accommodate data not part of the core resource. Common US Core extensions in the Patient resource include:   • US Core Race – Captures race according to US standards (based on CDC race and ethnicity codes).   • US Core Ethnicity – Captures patient ethnicity using OMB standards.   • US Core Birth Sex – Required to capture the patient’s birth sex (male, female, unknown).  These extensions support interoperability while meeting US-specific healthcare regulatory requirements. Bindings The US Core version of the Patient resource ensures that key fields are bound to standard US-specific terminologies for consistency:   • Gender – Bound to the `AdministrativeGender` value set (male, female, other, unknown), in line with global FHIR standards.   • Race and Ethnicity – Bound to the US Core Race and Ethnicity value sets, which adhere to CDC and OMB standards.   • Language – US Core specifies binding to the `Language` value set, covering common spoken and written languages used in the US healthcare system.  By adhering to these bindings, data remains consistent and easily sharable across systems. Value Sets   The US Core implementation also uses specific value sets to standardize data fields for national interoperability. These include:   • Contact Relationship – A value set used for defining relationships such as parent, spouse, guardian, etc., constrained for US usage.   • Address Use – Standardized value set that defines whether an address is for home, work, or temporary use in a way that meets US requirements.   • US Core Race/Ethnicity – Value sets for capturing detailed race and ethnicity information (OMB’s five race categories and specific CDC ethnicity codes). The FHIR Patient resource, particularly in the US Core implementation, strikes the perfect balance between standardization and adaptability. By leveraging profiles, extensions, bindings, and value sets, healthcare organizations can ensure patient data is not only comprehensive but also compliant with US standards, enabling seamless and compliant health information exchange.

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🌟 Generative AI and Healthcare Transformation: A New Era of Innovation 🌟 I am constantly asked about #AI applications for healthcare transformation. I continue to take people back to the pain points or problems being solved. The healthcare industry is undergoing a significant transformation, driven by the integration of generative AI. This revolutionary technology is currently reshaping the way we approach care management, early disease detection, and administrative tasks. Early Benefits of Generative AI: Enhanced Care Management: AI serves as a "co-pilot" for healthcare professionals, assisting in managing patient care more efficiently and accurately. This leads to improved patient outcomes and more personalized care plans. Early Disease Detection: AI's ability to analyze vast amounts of unstructured data enables early identification of diseases, potentially saving lives and reducing healthcare costs. It's a game-changer in preventive healthcare. Reducing Clinician Burnout: By automating repetitive administrative tasks, AI allows clinicians to focus more on patient care. This not only improves job satisfaction but also enhances the quality of care provided. Affordability and Efficiency: AI-driven processes streamline operations, making healthcare more affordable and accessible. From optimizing workflows to reducing operational costs, AI is paving the way for a more sustainable healthcare system. Challenges and Considerations: Ethical and Legal Frameworks: Ensuring AI is used responsibly requires robust ethical, legal, and policy frameworks. Trustworthy AI adoption is crucial for patient safety and health equity. Technology Infrastructure: Organizations need to update their technology infrastructure, including cloud capabilities, to fully support AI implementation. As we embrace these advancements, it's essential to stay informed and engaged in the conversation about how AI can continue to transform healthcare for the better. #HealthcareTransformation #GenerativeAI #HealthTech #Innovation #AIinHealthcare Thanks to sources McKinsey & Company (https://lnkd.in/g7HZHEST) and MedCity News (https://lnkd.in/gi95t4qJ)

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I’ve spoken with 50+ CMIOs / CIOs over the past year on how they’ve piloted new Health Tech for clinicians (e.g. AI scribes, generative AI drafts for patient portal messages, etc.). There’s no one right answer, but there are 5 main approaches I’ve heard for choosing clinicians to pilot with: 𝟭. 𝗠𝗼𝘀𝘁 𝘁𝗲𝗰𝗵 𝘀𝗮𝘃𝘃𝘆 𝗰𝗹𝗶𝗻𝗶𝗰𝗶𝗮𝗻𝘀 You want to move fast so you look for clinicians who raise their hand and are eager to try the solution. The risk? Tech savvy clinicians may be biased in favour of new tech. So they may not be highly representative of the population. 𝟮. 𝗟𝗲𝗮𝘀𝘁 𝘁𝗲𝗰𝗵 𝘀𝗮𝘃𝘃𝘆 𝗰𝗹𝗶𝗻𝗶𝗰𝗶𝗮𝗻𝘀 Or you do the opposite. If the tech is so intuitive, so easy to use that even the least tech savvy folks can and will use it… then you can be confident it can scale enterprise-wide. The risk? Maybe you don’t actually care if the least tech savvy folks can use it, because even 50% adoption is good enough to have a meaningful ROI. E.g. data I’ve seen shows clinician adoption of AI scribes can range 40% to 80% at health systems. But if you piloted with the least likely users (who don’t end up using it)… you would call the pilot a failure and miss out on the up to 80% who would’ve used it. 𝟯. 𝗖𝗹𝗶𝗻𝗶𝗰𝗶𝗮𝗻𝘀 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗽𝗮𝗶𝗻 𝗽𝗼𝗶𝗻𝘁𝘀 If you’re looking to demonstrate the biggest ROI, you could pilot with the clinicians who would benefit the most. For Generative AI drafts for patient portal messages, this is often primary care physicians as they spend more time in their inbox than other specialties. For AI Scribes you could check EHR data to see who is spending the most time on clinical documentation, who is doing the most pajama time in the EHR, etc. The risk? You might end up investing in a solution that works for one clinical group but not for others. 𝟰. 𝗟𝗮𝗿𝗴𝗲𝘀𝘁 𝗴𝗿𝗼𝘂𝗽 𝗼𝗳 𝗰𝗹𝗶𝗻𝗶𝗰𝗶𝗮𝗻𝘀 More data = shorter time to determining value. So it would make sense to pilot with largest specialty with the most clinicians. The risk? Again you would be assuming a solution or approach that works in one specialty is scalable to all… it may not be. 𝟱. 𝗦𝗼𝗺𝗲 𝘂𝗻𝗶𝗾𝘂𝗲 𝗶𝗻𝘀𝗶𝗴𝗵𝘁 𝗮𝗯𝗼𝘂𝘁 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝘂𝘀𝗲𝗿𝘀 For example some health systems have chosen AI Scribe pilot groups by asking medical office assistants to identify physicians who care the least about how a note is written and thus are more willing to delegate to an AI scribe. The risk? It’s super custom to the situation and it may be hard to replicate this approach with various new tech. 𝙒𝙝𝙖𝙩 𝙖𝙥𝙥𝙧𝙤𝙖𝙘𝙝 𝙬𝙤𝙪𝙡𝙙 𝙮𝙤𝙪𝙧 𝙝𝙚𝙖𝙡𝙩𝙝 𝙨𝙮𝙨𝙩𝙚𝙢 𝙩𝙖𝙠𝙚? #digitalhealth #healthcareinnovation

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India’s entrepreneurial ecosystem is thriving, offering vast, untapped potential. Unlike China, where state control has tightened, India provides a more supportive environment for startups. Here are some key points: ✅ 1. Growing Startup Ecosystem India’s startup ecosystem is the third-largest globally, with over 100 unicorns valued at around $340 billion. The surge in new businesses is driven by a young population, increasing internet use, and supportive government policies. ✅ 2. Untapped Markets The “Bharat” segment, referring to the population outside major cities, presents significant opportunities. Startups are targeting this segment with affordable products and services, bridging the digital divide between urban and rural areas. ✅ 3. Deep Science and Technology India’s deep science tech ecosystem is ripe for investment. Innovations in biotechnology, renewable energy, and advanced manufacturing are set to make global impacts. 📶 Recent Examples ✅ Zepto: Offers 10-minute grocery delivery, quickly becoming a unicorn. Jio Financial Services: Partnered with BlackRock to revolutionize wealth management in India. ✅ Semiconductor Startups: AGNIT Semiconductors and Aura Semiconductor are leading the industry. ✅ Dharaksha Ecosolutions: Converts crop residue into biodegradable packaging. ✅ Digital Payments and Agri-tech: Paytm and Ninjacart are transforming digital transactions and agricultural supply chains. India’s startup ecosystem is not just about financial growth but also societal progress. With the right investments, India is poised to become a global leader in innovation and entrepreneurship. if you are looking for corporate venture strategies or you are a startup or innovation looking for corporate ventures funding in India. Feel free to connect. [email protected]

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The authors' recent commentary discovers the evolving relationship between patients and the burgeoning field of AI in healthcare. As AI's influence on patient care becomes more tangible, this content shows the need for incorporating patient perspectives. That helps harmonize tech with patient-centric care, revealing opportunities and challenges. 1️⃣ Expanding the Dialogue on AI. Despite AI's promise, patients still lack a clear understanding of its practical benefits. That makes it crucial for ongoing dialogues to demystify AI and integrate it effectively into healthcare systems. 2️⃣ Bridging Gaps in AI Perception. There is a general enthusiasm for AI's potential in delivering personalized care and enhancing system efficiency. However, concerns exist regarding reduced human interaction and the potential for increased healthcare costs. 3️⃣ Addressing Privacy and Bias. Patients express concerns about data privacy and algorithmic bias. That highlights the need for robust governance and transparent AI systems prioritizing patient safety and data integrity. 4️⃣ Patient-Centric AI Development. Engaging patients in AI development from the outset can ensure that techs are aligned with their needs and ethical standards, enhancing trust and utility. 5️⃣ Strategic Patient Involvement. Effective patient involvement can steer AI research, ensuring that apps are created with a genuine understanding of patient needs and contexts. That promotes broader acceptance and integration. 🔗 By advocating for a model that prioritizes patient experiences and needs, the authors aim to leverage a healthcare evolution that is technologically advanced and deeply human-centric. For medical professionals, policymakers, and AI developers, embracing these insights can pave the way for more responsive and responsible AI solutions. 📄 Jennifer Catherine Louise Camaradou, Henry David Jeffry Hogg. Commentary: Patient Perspectives on Artificial Intelligence; What have We Learned and How Should We Move Forward? Advances in Therapy, vol. 40, 2024. ✅ Subscribe to my newsletter and dive deeper into the digital transformation of healthcare: https://lnkd.in/e6jujarm Interested in integrating effective and secure AI-driven technologies in your healthcare practice? Write to me to discuss the details, and my expert team will gladly assist you with your project! #ArtificialIntelligence #AIinHealthcare #DigitalHealth #HealthTech

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